SC-VAR: a computational tool for interpreting polygenic disease risks using single-cell epigenomic data.

ABSTRACT:

Motivation

One major challenge of interpreting variants from genome-wide association studies (GWAS) of complex traits or diseases is how to efficiently annotate noncoding variants. These variants influence gene expression by disrupting cis-regulatory elements (CREs), whose spatial and cell-type specificity are not adequately captured by conventional tools like multi-marker analysis of genomic annotation. Current methods either rely on linear proximity to genes or quantitative trait locus (QTL) data yet fail to integrate single-cell epigenomic information for a comprehensive annotation.

Results

We present SC-VAR, a novel computational tool designed to enhance the interpretation of disease-associated risks from GWAS using single-cell epigenomic data. SC-VAR leverages single-ce

SUBMITTER: Zhao G 

PROVIDER: S-EPMC11932087 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature